BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM

TL;DR

BusterX++ is a unified framework leveraging multimodal large language models and reinforcement learning to detect and explain synthetic images and videos, addressing limitations of single-modality detection methods.

Contribution

The paper introduces BusterX++, a novel unified detection framework for multimodal synthetic media, and GenBuster++, a comprehensive benchmark for evaluation.

Findings

Effective detection of synthetic images and videos.

High accuracy and generalizability demonstrated.

Benchmark with 4,000 curated media samples.

Abstract

Recent advances in generative AI have dramatically improved image and video synthesis capabilities, significantly increasing the risk of misinformation through sophisticated fake content. In response, detection methods have evolved from traditional approaches to multimodal large language models (MLLMs), offering enhanced transparency and interpretability in identifying synthetic media. However, current detection systems remain fundamentally limited by their single-modality design. These approaches analyze images or videos separately, making them ineffective against synthetic content that combines multiple media formats. To address these challenges, we introduce \textbf{BusterX++}, a framework for unified detection and explanation of synthetic image and video, with a direct reinforcement learning (RL) post-training strategy. To enable comprehensive evaluation, we also present \textbf{GenBuster++}, a unified benchmark leveraging state-of-the-art image and video generation techniques. This benchmark comprises 4,000 images and video clips, meticulously curated by human experts to ensure high quality, diversity, and real-world applicability. Extensive experiments demonstrate the effectiveness and generalizability of our approach.